Adaptive Smoothing Based on Gaussian Processes Regression Increases the Sensitivity and Specificity of fMRI Data

作者：Strappini Francesca^*; Gilboa Elad; Pitzalis Sabrina; Kay Kendrick; McAvoy Mark; Nehorai Arye; Snyder Abraham Z

来源：Human Brain Mapping, 2017, 38(3): 1438-1459.

DOI：10.1002/hbm.23464

• fMRI smoothing
• Gaussian processes regression
• denoising
• retinotopic mapping
• classification
• visual cortex
• early visual areas
• multivoxel pattern analysis
• searchlight

Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies.

• 出版日期2017-3